JEF Paper Coding (2022) - v3.3 Double Sort & Quintiles (For Monthly Data - Size & Operating Profit)

Size-Operating profitability portfolios are formed based on the NYSE, NASDAQ and AMEX.

Further improvement: + Change the risk-free rate data to the ones compatible with different frequency requirements.

Data cleaning and Preparation

Information about initial coding setup:

1. freq sets the data frequency for the following analysis, 12 for monthly data, 4 for quarterly data and 1 for annual data.
2. start.ym gives the earliest reasonable starting point of the series, which is January 1966, based on the available number of firms in the data set.
3. After the preliminary data cleaning, port_market is the market portfolio data (including NYSE, NASDAQ and AMEX), ports_all contains different deciles. All the data are stored in the file named as market.names and data.names. I’ve finished creating the measures based on characteristic deciles, so I’ll have a close look at your results shortly. The decile data is attached. As mentioned, these are based on a single characteristic sort, which will hopefully provide new insight into characteristic based predictability. The characteristics are as follows:

• Size (size)

• Book-to-market ratio (bm)

• Operating profitability (op)

• Investment (inv); and

• Momentum (mom)

  The filenames indicate which is which, and the quantiles are denoted from Q1 (low value on the characteristic) to Q5 (high value on the characteristic) with the breakpoints identified according to the NYSE distribution. The basis of the sorts are consistent with Fama and French (2015) (and their earlier work) in that re-sorting occurs only one a year. An alternative is to re-sort monthly, which I’ve done for other papers applying a similar approach, but doing so comes with its own drawbacks.

4. RF denotes the risk-free rate, which is the average of the bid and ask.

Notes: Seems that the big-value and small-growth portfolios include less firms comparing the other four characteristic portfolios, around half of them.

Figure 1 - Log Cumulative Index

Log cumulative realised portfolio return components for seven portfolios - the market portfolio and six size and book-to-market equity ratio sorted portfolios. All following figures demonstrate the monthly realised price-earnings ratio growth (gm), earnings growth (ge), dividend-price (dp) and the portfolio return index (r) with the values in January 1966 as zero for all portfolios.

.

Table 1 - Summary statistics of returns components

The correlations between gm and ge might be a bit too high comparing to Ferreira and Santa-Clara (2011). Need to check the code again.

Need to go back to the construction process of Prof Robert Shiller’s CAPE.

‘kable’ for Table Creation

## Warning: `columns = vars(...)` has been deprecated in gt 0.3.0:
## * please use `columns = c(...)` instead

## Warning: `columns = vars(...)` has been deprecated in gt 0.3.0:
## * please use `columns = c(...)` instead

Table 1 - Summary statistics of returns components
monthly data starts from Jan 1967 and ends in Dec 2019.
	Panel A: univariate statistics								Panel B: Correlations
	Mean	Median	SD	Min	Max	Skew	Kurt	AR(1)	gm	ge	dp	r
Market
gm	0.02	-0.03	3.12	-15.26	13.28	-0.19	4.42	0.92	1.00	-0.51	-0.03	0.07
ge	0.76	1.11	5.34	-22.01	19.34	-0.50	2.44	0.33	-0.51	1.00	-0.03	0.81
dp	0.28	0.27	0.09	0.09	0.50	0.14	-0.70	0.98	-0.03	-0.03	1.00	-0.03
r	0.94	1.25	4.43	-22.48	16.58	-0.51	1.85	0.05	0.07	0.81	-0.03	1.00
B_Q1
gm	0.12	0.14	6.19	-23.91	35.24	-0.03	3.09	0.68	1.00	-0.72	-0.01	0.05
ge	0.59	0.43	8.19	-34.11	30.68	-0.04	1.83	0.43	-0.72	1.00	-0.03	0.64
dp	0.28	0.29	0.18	0.03	1.11	0.84	0.37	0.94	-0.01	-0.03	1.00	-0.02
r	0.79	1.11	5.43	-25.81	19.81	-0.54	2.11	0.08	0.05	0.64	-0.02	1.00
B_Q2
gm	0.09	-0.07	3.96	-15.20	14.08	-0.05	1.74	0.86	1.00	-0.62	0.01	0.07
ge	0.58	1.03	5.79	-24.33	21.64	-0.23	1.70	0.39	-0.62	1.00	-0.04	0.71
dp	0.30	0.29	0.13	0.09	0.62	0.32	-1.15	0.98	0.01	-0.04	1.00	-0.02
r	0.86	1.13	4.40	-20.67	17.73	-0.37	1.70	0.04	0.07	0.71	-0.02	1.00
B_Q3
gm	-0.07	-0.37	3.50	-11.81	27.48	1.70	9.72	0.70	1.00	-0.55	-0.07	0.09
ge	0.72	1.19	5.56	-28.07	27.36	-0.47	3.01	0.25	-0.55	1.00	-0.01	0.76
dp	0.27	0.28	0.11	0.06	0.64	0.28	-0.59	0.95	-0.07	-0.01	1.00	-0.03
r	0.89	1.05	4.43	-21.79	18.44	-0.36	1.87	0.03	0.09	0.76	-0.03	1.00
B_Q4
gm	0.02	0.21	1.88	-7.31	6.96	-0.25	0.51	0.76	1.00	-0.22	-0.15	0.18
ge	0.70	0.85	4.66	-21.90	22.90	-0.14	1.94	0.05	-0.22	1.00	0.00	0.91
dp	0.24	0.22	0.08	0.06	0.47	0.41	-0.59	0.98	-0.15	0.00	1.00	-0.04
r	0.95	1.29	4.51	-19.13	20.27	-0.32	1.49	0.00	0.18	0.91	-0.04	1.00
B_Q5
gm	0.01	-0.01	1.87	-7.49	5.96	-0.14	0.59	0.89	1.00	-0.19	-0.21	0.21
ge	0.80	0.72	4.46	-24.13	20.67	-0.22	2.20	0.11	-0.19	1.00	-0.01	0.91
dp	0.23	0.21	0.08	0.09	0.47	0.79	-0.04	0.97	-0.21	-0.01	1.00	-0.06
r	1.01	1.15	4.41	-22.64	16.89	-0.43	1.95	0.05	0.21	0.91	-0.06	1.00
S_Q1
gm	-0.39	-0.03	12.28	-107.94	126.87	-0.54	50.94	0.31	1.00	-0.85	-0.07	0.09
ge	1.46	1.56	13.61	-122.30	105.86	0.01	30.42	0.20	-0.85	1.00	0.02	0.43
dp	0.37	0.31	0.24	0.09	1.87	2.69	10.44	1.00	-0.07	0.02	1.00	-0.08
r	0.92	1.15	6.94	-32.81	37.63	-0.13	2.57	0.14	0.09	0.43	-0.08	1.00
S_Q2
gm	0.08	-0.09	13.56	-123.31	122.26	-0.14	30.60	0.49	1.00	-0.92	-0.04	0.11
ge	1.02	1.04	14.06	-117.78	129.95	0.16	26.12	0.41	-0.92	1.00	0.01	0.29
dp	0.28	0.27	0.11	0.10	0.67	0.44	-0.43	0.95	-0.04	0.01	1.00	-0.03
r	1.16	1.59	5.42	-29.29	25.39	-0.56	2.73	0.11	0.11	0.29	-0.03	1.00
S_Q3
gm	0.12	-0.10	6.18	-55.33	65.76	1.53	42.03	0.72	1.00	-0.72	-0.04	0.10
ge	1.05	1.40	7.76	-61.31	58.78	-0.78	14.60	0.40	-0.72	1.00	-0.03	0.61
dp	0.27	0.25	0.12	0.09	0.83	1.19	2.59	0.93	-0.04	-0.03	1.00	-0.07
r	1.22	1.41	5.29	-26.25	25.59	-0.47	2.40	0.11	0.10	0.61	-0.07	1.00
S_Q4
gm	-0.04	-0.12	6.65	-93.18	57.68	-2.61	81.20	0.65	1.00	-0.74	-0.10	0.05
ge	1.25	1.60	8.58	-54.17	93.77	1.31	28.66	0.45	-0.74	1.00	0.02	0.62
dp	0.26	0.25	0.10	0.07	0.57	0.60	0.31	0.93	-0.10	0.02	1.00	-0.06
r	1.20	1.59	5.51	-28.11	26.89	-0.51	2.53	0.14	0.05	0.62	-0.06	1.00
S_Q5
gm	-0.14	0.05	7.50	-30.30	55.63	1.06	10.08	0.77	1.00	-0.74	-0.18	0.09
ge	1.51	1.58	9.32	-51.00	33.54	-0.59	4.20	0.48	-0.74	1.00	0.10	0.59
dp	0.30	0.24	0.21	0.08	1.74	3.06	12.33	0.96	-0.18	0.10	1.00	-0.05
r	1.30	1.61	6.15	-30.27	27.79	-0.45	2.57	0.15	0.09	0.59	-0.05	1.00
Note: Panel A in this table presents mean, median, standard deviation (SD), minimum, maximum, skewness (Skew), kurtosis (kurt) and first-order autocorrelation coefficient of the realised components of stock market returns and six size and book-to-market equity ratio sorted portfolios. These univariate statistics for each portfolios are presented separately. gm is the continuously compounded growth rate in the price-earnings ratio. ge is the continuously compounded growth rate in earnings. dp is the log of one plus the dividend-price ratio. *r* is the portfolio returns. Panel B in this table reports correlation matrices for all seven portfolios. The sample period starts from Feburary 1966 and ends in December 2019.

Figure 3 - Cumulative OOS R-sqaure Difference and Cumulative SSE Difference

The cumulative OOS R-square figures show the out-of-sample cumulative R-square up to each month from predictive regressions with listed predictors and from the sum-of-the-parts (SOP) method for each portfolio. The cumulative SSE difference plots indicates the out-of-sample performance of each model. These are evaluated by the cumulative squared prediction errors of the NULL minus the cumulative squared predictirion error of the ALTERNATIVE. The NULL model is the historical mean model, while the ALTERNATIVE model is either the predictive regression model or the SOP model. An incresae in the line suggests better performance of the ALTERNATIVE model and a decrease suggests that the NULL model is better.

Several points to note in the coding:

1. The dividend-price ratio (‘DP’ hereafter) is calculated as the log of 1 plus the frequency-adjusted dividend to price ratio, rather than using the annual dividend. As by this return decomposition, the expected amount of dividend payout in each period should be adjusted by the frequency of the data in the analysis. \[ dp_t = \log (1 + \frac{\tilde{D}_t}{P_t}) = \log (1 + \frac{D_t / n}{P_t}) \text{,} \] where \(D_t\) is the annual dividend payment and \(n\) is the data frequency (e.g. \(n = 1\) for annual data and \(n = 12\) for monthly data) and \(\tilde{D}_t\) is the freqency-adjusted dividend payment for period \(t\).

2. The SOP method by Ferreira and Santa-Clara (2011) decomposes the portfolio return into three components, namely the earnings growth, the prie multiple expansion and the next period dividend-price ratio. Here to generate the SOP prediction, we use the rolling mean of past earnings growth as the expected growth of the next period (denoted as ge1). However, there are other choices, such as recursive means in ge2 and ge3.

3. critica.value = TRUE is the option whether to use boostrap method to calculate the MSE-F critical values. This is used in function Boot_MSE.F.

4. The authors should evaluate the significance of the MSE−F statistic by using the theoret- ical distribution derived in McCracken (2007). The bootstrap-based inference (presented in Pages 9-10) can represent a robustness check and moved to an appendix. Further- more, the authors can also include in the main results the related out-of-sample statistic proposed by Clark and West (2007), which follows a standard Normal distribution. Therefore, readjust the Boot_MSE.F function.

5. Column McCracken in Table 2 (line 604) gives the significance of the out-of-sample \(MSE–F\) statistic of McCracken (2007). \(***\), \(**\), and \(*\) denote significance at the 1%, 5%, and 10% level, respectively. Please refer to the Table 4 on P749 in McCracken (2007) with \(k_2 = 1\) and \(\pi = P/R = \frac{\text{Number of out-of-sample forecasts}}{\text{Number of observations used to form the first forecast}} = 1.6\).

• Examples are in P742-743. See detailed notations

## [1] "market_Allfirms.csv"
## [1] "Market"
## ##------ Mon Aug  8 10:48:43 2022 ------##

## Note: Using an external vector in selections is ambiguous.
## ℹ Use `all_of(actual)` instead of `actual` to silence this message.
## ℹ See <https://tidyselect.r-lib.org/reference/faq-external-vector.html>.
## This message is displayed once per session.
## Note: Using an external vector in selections is ambiguous.
## ℹ Use `all_of(cond)` instead of `cond` to silence this message.
## ℹ See <https://tidyselect.r-lib.org/reference/faq-external-vector.html>.
## This message is displayed once per session.
## Note: Using an external vector in selections is ambiguous.
## ℹ Use `all_of(uncond)` instead of `uncond` to silence this message.
## ℹ See <https://tidyselect.r-lib.org/reference/faq-external-vector.html>.
## This message is displayed once per session.
## Note: Using an external vector in selections is ambiguous.
## ℹ Use `all_of(x)` instead of `x` to silence this message.
## ℹ See <https://tidyselect.r-lib.org/reference/faq-external-vector.html>.
## This message is displayed once per session.

## [1] "OOS R Squared: 0.0047"
## [1] "MSE-F: 1.8524"

## Note: Using an external vector in selections is ambiguous.
## ℹ Use `all_of(predictor)` instead of `predictor` to silence this message.
## ℹ See <https://tidyselect.r-lib.org/reference/faq-external-vector.html>.
## This message is displayed once per session.

## [1] "IS R Squared: 0.0106"
## [1] "OOS R Squared: -0.0104"
## [1] "MSE-F: -4.054"
## [1] "IS R Squared: 0.0045"
## [1] "OOS R Squared: -0.0023"
## [1] "MSE-F: -0.9222"
## [1] "IS R Squared: 0.0051"
## [1] "OOS R Squared: -0.0017"
## [1] "MSE-F: -0.658"
## [1] "IS R Squared: 0.012"
## [1] "OOS R Squared: -0.0086"
## [1] "MSE-F: -3.3515"
## [1] "IS R Squared: 1e-04"
## [1] "OOS R Squared: -0.0079"
## [1] "MSE-F: -3.1035"

## [1] "port_B_Q1.csv"
## [1] "B_Q1"
## ##------ Mon Aug  8 10:48:49 2022 ------##
## [1] "OOS R Squared: 0.002"
## [1] "MSE-F: 0.792"

## [1] "IS R Squared: 0.007"
## [1] "OOS R Squared: -0.0086"
## [1] "MSE-F: -3.3625"
## [1] "IS R Squared: 0.001"
## [1] "OOS R Squared: -0.0045"
## [1] "MSE-F: -1.776"
## [1] "IS R Squared: 0.0016"
## [1] "OOS R Squared: -0.004"
## [1] "MSE-F: -1.5907"
## [1] "IS R Squared: 0.0082"
## [1] "OOS R Squared: -0.0065"
## [1] "MSE-F: -2.561"
## [1] "IS R Squared: 0.0076"
## [1] "OOS R Squared: -0.0041"
## [1] "MSE-F: -1.5991"

## [1] "port_B_Q2.csv"
## [1] "B_Q2"
## ##------ Mon Aug  8 10:48:54 2022 ------##
## [1] "OOS R Squared: 0.0116"
## [1] "MSE-F: 4.6675"

## [1] "IS R Squared: 0.0062"
## [1] "OOS R Squared: -0.0173"
## [1] "MSE-F: -6.7025"
## [1] "IS R Squared: 0.0047"
## [1] "OOS R Squared: 8e-04"
## [1] "MSE-F: 0.3154"
## [1] "IS R Squared: 0.005"
## [1] "OOS R Squared: 0.0017"
## [1] "MSE-F: 0.6915"
## [1] "IS R Squared: 0.0067"
## [1] "OOS R Squared: -0.0141"
## [1] "MSE-F: -5.4749"
## [1] "IS R Squared: 0"
## [1] "OOS R Squared: -0.0043"
## [1] "MSE-F: -1.6808"

## [1] "port_B_Q3.csv"
## [1] "B_Q3"
## ##------ Mon Aug  8 10:49:00 2022 ------##
## [1] "OOS R Squared: -4e-04"
## [1] "MSE-F: -0.1475"

## [1] "IS R Squared: 0.0064"
## [1] "OOS R Squared: -0.0259"
## [1] "MSE-F: -9.9875"
## [1] "IS R Squared: 0.0041"
## [1] "OOS R Squared: -0.0015"
## [1] "MSE-F: -0.5876"
## [1] "IS R Squared: 0.0041"
## [1] "OOS R Squared: -8e-04"
## [1] "MSE-F: -0.3273"
## [1] "IS R Squared: 0.0065"
## [1] "OOS R Squared: -0.0205"
## [1] "MSE-F: -7.9465"
## [1] "IS R Squared: 1e-04"
## [1] "OOS R Squared: -0.0035"
## [1] "MSE-F: -1.3745"

## [1] "port_B_Q4.csv"
## [1] "B_Q4"
## ##------ Mon Aug  8 10:49:05 2022 ------##
## [1] "OOS R Squared: 0.0035"
## [1] "MSE-F: 1.387"

## [1] "IS R Squared: 0.0067"
## [1] "OOS R Squared: -0.0141"
## [1] "MSE-F: -5.5"
## [1] "IS R Squared: 0.0071"
## [1] "OOS R Squared: -0.0024"
## [1] "MSE-F: -0.9588"
## [1] "IS R Squared: 0.0069"
## [1] "OOS R Squared: -0.0016"
## [1] "MSE-F: -0.6438"
## [1] "IS R Squared: 0.0067"
## [1] "OOS R Squared: -0.0124"
## [1] "MSE-F: -4.8507"
## [1] "IS R Squared: 2e-04"
## [1] "OOS R Squared: -0.0054"
## [1] "MSE-F: -2.1129"

## [1] "port_B_Q5.csv"
## [1] "B_Q5"
## ##------ Mon Aug  8 10:49:11 2022 ------##
## [1] "OOS R Squared: 0.0028"
## [1] "MSE-F: 1.1289"

## [1] "IS R Squared: 0.0043"
## [1] "OOS R Squared: -0.0029"
## [1] "MSE-F: -1.1461"
## [1] "IS R Squared: 0.0076"
## [1] "OOS R Squared: 0.0052"
## [1] "MSE-F: 2.0658"
## [1] "IS R Squared: 0.0086"
## [1] "OOS R Squared: 0.0062"
## [1] "MSE-F: 2.4823"
## [1] "IS R Squared: 0.0052"
## [1] "OOS R Squared: -0.0021"
## [1] "MSE-F: -0.8418"
## [1] "IS R Squared: 8e-04"
## [1] "OOS R Squared: -0.0036"
## [1] "MSE-F: -1.4095"

## [1] "port_S_Q1.csv"
## [1] "S_Q1"
## ##------ Mon Aug  8 10:49:16 2022 ------##
## [1] "OOS R Squared: -0.0186"
## [1] "MSE-F: -7.2325"

## [1] "IS R Squared: 0.0019"
## [1] "OOS R Squared: -0.0135"
## [1] "MSE-F: -5.269"
## [1] "IS R Squared: 2e-04"
## [1] "OOS R Squared: -0.0014"
## [1] "MSE-F: -0.5505"
## [1] "IS R Squared: 5e-04"
## [1] "OOS R Squared: -0.001"
## [1] "MSE-F: -0.4085"
## [1] "IS R Squared: 0.0038"
## [1] "OOS R Squared: -0.0169"
## [1] "MSE-F: -6.564"
## [1] "IS R Squared: 1e-04"
## [1] "OOS R Squared: -0.002"
## [1] "MSE-F: -0.773"

## [1] "port_S_Q2.csv"
## [1] "S_Q2"
## ##------ Mon Aug  8 10:49:21 2022 ------##
## [1] "OOS R Squared: -0.0057"
## [1] "MSE-F: -2.2591"

## [1] "IS R Squared: 0.0101"
## [1] "OOS R Squared: -0.0119"
## [1] "MSE-F: -4.6303"
## [1] "IS R Squared: 0.0032"
## [1] "OOS R Squared: -0.0141"
## [1] "MSE-F: -5.4772"
## [1] "IS R Squared: 0.0041"
## [1] "OOS R Squared: -0.0162"
## [1] "MSE-F: -6.2895"
## [1] "IS R Squared: 0.0128"
## [1] "OOS R Squared: -0.0158"
## [1] "MSE-F: -6.1477"
## [1] "IS R Squared: 0"
## [1] "OOS R Squared: -0.0116"
## [1] "MSE-F: -4.5471"

## [1] "port_S_Q3.csv"
## [1] "S_Q3"
## ##------ Mon Aug  8 10:49:26 2022 ------##
## [1] "OOS R Squared: -0.0044"
## [1] "MSE-F: -1.7474"

## [1] "IS R Squared: 0.0024"
## [1] "OOS R Squared: -0.0106"
## [1] "MSE-F: -4.1428"
## [1] "IS R Squared: 0.0046"
## [1] "OOS R Squared: -0.0036"
## [1] "MSE-F: -1.4168"
## [1] "IS R Squared: 0.006"
## [1] "OOS R Squared: -0.0045"
## [1] "MSE-F: -1.7582"
## [1] "IS R Squared: 0.0037"
## [1] "OOS R Squared: -0.015"
## [1] "MSE-F: -5.8306"
## [1] "IS R Squared: 4e-04"
## [1] "OOS R Squared: -0.0073"
## [1] "MSE-F: -2.8714"

## [1] "port_S_Q4.csv"
## [1] "S_Q4"
## ##------ Mon Aug  8 10:49:31 2022 ------##
## [1] "OOS R Squared: -0.0021"
## [1] "MSE-F: -0.841"

## [1] "IS R Squared: 0.0057"
## [1] "OOS R Squared: -0.0155"
## [1] "MSE-F: -6.0385"
## [1] "IS R Squared: 0.0038"
## [1] "OOS R Squared: -0.0028"
## [1] "MSE-F: -1.087"
## [1] "IS R Squared: 0.0061"
## [1] "OOS R Squared: -0.0033"
## [1] "MSE-F: -1.2823"
## [1] "IS R Squared: 0.009"
## [1] "OOS R Squared: -0.0222"
## [1] "MSE-F: -8.5895"
## [1] "IS R Squared: 0"
## [1] "OOS R Squared: -0.0071"
## [1] "MSE-F: -2.7826"

## [1] "port_S_Q5.csv"
## [1] "S_Q5"
## ##------ Mon Aug  8 10:49:35 2022 ------##
## [1] "OOS R Squared: -0.0031"
## [1] "MSE-F: -1.2259"

## [1] "IS R Squared: 0.0056"
## [1] "OOS R Squared: -0.0218"
## [1] "MSE-F: -8.4223"
## [1] "IS R Squared: 0.0118"
## [1] "OOS R Squared: 0.0069"
## [1] "MSE-F: 2.7253"
## [1] "IS R Squared: 0.0154"
## [1] "OOS R Squared: 0.008"
## [1] "MSE-F: 3.2033"
## [1] "IS R Squared: 0.0085"
## [1] "OOS R Squared: -0.0274"
## [1] "MSE-F: -10.5191"
## [1] "IS R Squared: 8e-04"
## [1] "OOS R Squared: -0.0135"
## [1] "MSE-F: -5.2706"

Table 2 - Forecasts of portfolio returns

This table demonstrates the in-sample and out-of-sample R-squares for the market and six size and book-to-market equity ratio sorted portfolios from predictive regressions and the Sum-of-the-Parts method. IS R-squares are estimated using the whole sample period and the OOS R-squares are calculated compare the forecast error of the model against the historical mean model. The full sample period starts from Feb 1966 to December 2019 and the IS period is set to be 20 years with forecsats beginning in Feb 1986. The MSE-F statistics are calculated to test the hypothesis \(H_0: \text{out-of-sample R-squares} = 0\) vs \(H_1: \text{out-of-sample R-squares} \neq 0\).

Predictors here are all in log terms.

gt(table2.df, rowname_col = "rowname", groupname_col = "portname") %>%
  tab_header(title = "Table 2 - Forecasts of portfolio returns",
             subtitle = paste(freq_name(freq = freq), " data starts from ", first(data_decompose$month), " and ends in ", last(data_decompose$month), ".", sep = "")) %>%
  fmt_number(columns = 1:4, decimals = 6, suffixing = TRUE)

Table 2 - Forecasts of portfolio returns
monthly data starts from Jan 1967 and ends in Dec 2019.
	IS_r.squared	OOS_r.squared	MAE_A	MSE_F	McCracken
Market
DP	0.010630	−0.010370	0.032611	−4.054026
PE	0.004546	−0.002340	0.032475	−0.922206
EY	0.005117	−0.001669	0.032494	−0.657989
DY	0.011952	−0.008557	0.032621	−3.351461
Payout	0.000117	−0.007919	0.032081	−3.103538
SOP	NA	0.004656	0.032173	1.852440	**
B_Q1
DP	0.007038	−0.008586	0.041865	−3.362542
PE	0.001020	−0.004516	0.040838	−1.775964
EY	0.001568	−0.004043	0.040842	−1.590743
DY	0.008173	−0.006526	0.041894	−2.561039
Payout	0.007584	−0.004065	0.041369	−1.599054
SOP	NA	0.001996	0.041316	0.792046	*
B_Q2
DP	0.006247	−0.017261	0.032523	−6.702477
PE	0.004666	0.000798	0.031741	0.315387
EY	0.004972	0.001748	0.031719	0.691547	*
DY	0.006725	−0.014055	0.032470	−5.474863
Payout	0.000035	−0.004273	0.031463	−1.680764
SOP	NA	0.011649	0.031632	4.667491	***
B_Q3
DP	0.006391	−0.025941	0.033777	−9.987465
PE	0.004061	−0.001490	0.033054	−0.587592
EY	0.004094	−0.000829	0.033044	−0.327306
DY	0.006510	−0.020531	0.033689	−7.946487
Payout	0.000135	−0.003492	0.032929	−1.374517
SOP	NA	−0.000373	0.033122	−0.147522
B_Q4
DP	0.006650	−0.014121	0.033243	−5.499987
PE	0.007095	−0.002433	0.032917	−0.958831
EY	0.006938	−0.001633	0.032883	−0.643808
DY	0.006697	−0.012433	0.033228	−4.850710
Payout	0.000187	−0.005378	0.032574	−2.112856
SOP	NA	0.003490	0.032798	1.387023	*
B_Q5
DP	0.004259	−0.002910	0.031048	−1.146070
PE	0.007635	0.005203	0.030926	2.065835	**
EY	0.008566	0.006245	0.030942	2.482277	**
DY	0.005157	−0.002136	0.031086	−0.841772
Payout	0.000789	−0.003581	0.030891	−1.409507
SOP	NA	0.002843	0.031088	1.128890	*
S_Q1
DP	0.001876	−0.013520	0.051547	−5.269008
PE	0.000156	−0.001396	0.051241	−0.550486
EY	0.000518	−0.001035	0.051176	−0.408474
DY	0.003760	−0.016899	0.051652	−6.564000
Payout	0.000132	−0.001961	0.051315	−0.772999
SOP	NA	−0.018604	0.051198	−7.232487
S_Q2
DP	0.010074	−0.011861	0.038837	−4.630349
PE	0.003239	−0.014061	0.038563	−5.477153
EY	0.004112	−0.016181	0.038634	−6.289547
DY	0.012822	−0.015810	0.038933	−6.147652
Payout	0.000000	−0.011646	0.038129	−4.547095
SOP	NA	−0.005737	0.038230	−2.259062
S_Q3
DP	0.002354	−0.010599	0.038400	−4.142827
PE	0.004554	−0.003600	0.038197	−1.416833
EY	0.006028	−0.004471	0.038332	−1.758228
DY	0.003701	−0.014982	0.038602	−5.830584
Payout	0.000404	−0.007323	0.038036	−2.871417
SOP	NA	−0.004432	0.038052	−1.747422
S_Q4
DP	0.005709	−0.015525	0.039190	−6.038486
PE	0.003819	−0.002760	0.038556	−1.087033
EY	0.006110	−0.003257	0.038615	−1.282300
DY	0.009027	−0.022229	0.039376	−8.589484
Payout	0.000028	−0.007094	0.038661	−2.782582
SOP	NA	−0.002128	0.038422	−0.841011
S_Q5
DP	0.005632	−0.021787	0.041366	−8.422304
PE	0.011825	0.006852	0.041837	2.725262	**
EY	0.015380	0.008044	0.041886	3.203271	**
DY	0.008545	−0.027359	0.041397	−10.519143
Payout	0.000757	−0.013524	0.041727	−5.270570
SOP	NA	−0.003105	0.041164	−1.225935

Figure 4 - Monthly return predictions

Here I only present the monthly predictions of the historical mean model, the SOP method and the predictive regressions based on the dividend-price ratio and the earnings-price ratio.

## [1] "market_Allfirms.csv"
## [1] "Market"
## ##------ Mon Aug  8 10:49:46 2022 ------##

## [1] "port_B_Q1.csv"
## [1] "B_Q1"
## ##------ Mon Aug  8 10:49:51 2022 ------##

## [1] "port_B_Q2.csv"
## [1] "B_Q2"
## ##------ Mon Aug  8 10:49:55 2022 ------##

## [1] "port_B_Q3.csv"
## [1] "B_Q3"
## ##------ Mon Aug  8 10:49:59 2022 ------##

## [1] "port_B_Q4.csv"
## [1] "B_Q4"
## ##------ Mon Aug  8 10:50:03 2022 ------##

## [1] "port_B_Q5.csv"
## [1] "B_Q5"
## ##------ Mon Aug  8 10:50:07 2022 ------##

## [1] "port_S_Q1.csv"
## [1] "S_Q1"
## ##------ Mon Aug  8 10:50:12 2022 ------##

## [1] "port_S_Q2.csv"
## [1] "S_Q2"
## ##------ Mon Aug  8 10:50:16 2022 ------##

## [1] "port_S_Q3.csv"
## [1] "S_Q3"
## ##------ Mon Aug  8 10:50:20 2022 ------##

## [1] "port_S_Q4.csv"
## [1] "S_Q4"
## ##------ Mon Aug  8 10:50:24 2022 ------##

## [1] "port_S_Q5.csv"
## [1] "S_Q5"
## ##------ Mon Aug  8 10:50:28 2022 ------##

Figure 5 - Trading Performance (with no trading restrictions)

• Markowitz optimal weight on the risky portfolio using the SOP method and the historical mean model.
• Markowitz optimal weight on the risky portfolio using all models.
• Out-of-sample performance of the trading strategies - the SOP method, the historical mean model and the buy-and-hold strategy - with no trading restrictions.

## Warning in window.default(x, ...): 'start' value not changed

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## Warning in xy.coords(x = matrix(rep.int(tx, k), ncol = k), y = x, log = log, :
## 393 y values <= 0 omitted from logarithmic plot

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## Warning in xy.coords(x = matrix(rep.int(tx, k), ncol = k), y = x, log = log, :
## 387 y values <= 0 omitted from logarithmic plot

## Warning in window.default(x, ...): 'start' value not changed

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Table 3 - Certaint equivalent gains

Trading Strategies: certaint equivalent gains

This table shows the out-of-sample portfolio choice results at monthly frequencies from predictive regressions and the SOP method. The trading strategy for each portfolio is designed by optimally allocating funds between the risk-free asset and the corresponding risky portfolio. The certainty equivalent return is \(\overline{rp} - \frac{1}{2} \gamma \hat{\sigma}_{rp}^{2}\) with a risk-aversion coefficient \(\gamma = 3\). The annualised certainty equivalent gain (in percentage) is the monthly certainty equivalent gain multiplied by the corresponding frequency (e.g. 12 for monthly data).

dt <- table3.df %>%
  filter(rowname %in% c(ratio_names, "sop_simple")) %>%
  select(CEGs_annualised, rowname, portname)

as.data.frame(matrix(dt$CEGs_annualised, byrow = F, nrow = length(ratio_names) + 1, ncol = length(id.names))) %>%
  `colnames<-`(unique(dt$portname)) %>%
  mutate(Variable = unique(dt$rowname)) %>%
  # round(digits = 4) %>%
  as.tbl() %>%
  select(Variable, unique(dt$portname)) %>%
  gt(rowname_col = "Variable") %>%
  tab_header(title = "Table 3 - Trading Strategies: certainty equivalent gains",
             subtitle = paste(str_to_title(freq_name(freq = freq)), " data starts from ", first(data_decompose$month) + 20, " and ends in ", last(data_decompose$month), ".", sep = "")) %>%
  fmt_percent(columns = 2:(length(id.names)+1), decimals = 2)

Table 3 - Trading Strategies: certainty equivalent gains
Monthly data starts from Jan 1987 and ends in Dec 2019.
	Market	B_Q1	B_Q2	B_Q3	B_Q4	B_Q5	S_Q1	S_Q2	S_Q3	S_Q4	S_Q5
sop_simple	1.10%	0.45%	5.54%	−0.74%	−6.42%	−0.64%	−15.71%	−2.19%	0.01%	−0.33%	−3.23%
DP	−2.85%	−3.16%	−2.51%	−5.81%	−4.50%	−3.52%	−11.21%	−4.61%	2.42%	−1.60%	−4.14%
PE	0.84%	−2.58%	2.77%	1.25%	−1.25%	1.81%	−0.97%	−5.88%	2.18%	0.26%	−0.48%
EY	0.98%	−2.46%	2.89%	1.40%	−1.36%	2.03%	−1.39%	−6.60%	1.71%	−0.05%	−2.95%
DY	−2.36%	−3.02%	−2.27%	−4.60%	−5.16%	−3.82%	−15.18%	−10.21%	2.24%	−6.16%	−8.83%
Payout	−4.13%	−1.18%	−4.01%	−1.48%	−1.40%	−2.24%	0.14%	−2.10%	−0.63%	−0.03%	−8.52%

Table 4 - Sharpe ratio Gains

Trading Strategies: Sharpe ratio Gains

This table presents the Sharpe ratio of the out-of-sample performance of trading strategies, allocating funds between risk-free and risky assets for each portfolio. The annualised Sharpe ratio is generated by multipling the monthly Sharpe ratio by square root of the corresponding frequency (e.g. \(\sqrt{12}\) for monthly data).

dt <- table4.df %>%
  filter(rowname %in% c(ratio_names, "sop_simple")) %>%
  select(SRG_annualised, rowname, portname)

as.data.frame(matrix(dt$SRG_annualised, byrow = F, nrow = length(ratio_names) + 1, ncol = length(id.names))) %>%
  `colnames<-`(unique(dt$portname)) %>%
  mutate(Variable = unique(dt$rowname)) %>%
  # round(digits = 4) %>%
  as.tbl() %>%
  select(Variable, unique(dt$portname)) %>%
  gt(rowname_col = "Variable") %>%
  tab_header(title = "Table 4 - Trading Strategies: Sharpe ratio gains", 
             subtitle = paste(str_to_title(freq_name(freq = freq)), " data starts from ", first(data_decompose$month) + 20, " and ends in ", last(data_decompose$month), ".", sep = "")) %>%
  fmt_number(columns = 2:(length(id.names)+1), decimals = 4) 

Table 4 - Trading Strategies: Sharpe ratio gains
Monthly data starts from Jan 1987 and ends in Dec 2019.
	Market	B_Q1	B_Q2	B_Q3	B_Q4	B_Q5	S_Q1	S_Q2	S_Q3	S_Q4	S_Q5
sop_simple	0.0524	0.0253	0.3329	−0.0898	−0.1218	−0.0304	−0.0303	0.0229	0.0454	0.0360	0.0546
DP	−0.1667	−0.2247	−0.1869	−0.4316	−0.2309	−0.1666	−0.2276	−0.0668	0.0391	−0.0174	0.0374
PE	0.0674	−0.0409	0.1660	0.0515	−0.1227	0.1469	−0.0483	−0.1832	0.0693	−0.0064	0.0565
EY	0.0827	−0.0404	0.1784	0.0650	−0.1212	0.1742	−0.0401	−0.1920	0.0697	−0.0114	0.0516
DY	−0.1441	−0.2168	−0.1709	−0.3790	−0.2367	−0.1795	−0.2269	−0.1043	0.0277	−0.0624	0.0444
Payout	−0.1547	−0.0553	−0.1380	−0.0557	−0.0035	−0.0916	−0.0380	−0.0968	−0.0142	−0.0162	−0.2200

Figure 6 - Sensitivity of Certainty Equivalent Gains relative to Risk-Aversion level

This figure presents the out-of-sample portfolio choice results at monthly frequency from bivariate predictive regressions and the SOP method with different levels of risk-aversion. To show that our previous results hold with respect to investors with different levels of risk aversion, we evaluate the changes in certainty equivalent gains with respect to the changes in the level of risk-aversion. The results of the trading strategy reported here are without trading restrictions (as in Table 5), allocating funds between the risk-free asset and the risky equity portfolio. The portfolio choice results are evaluated in the certainty equivalent return with relative risk-aversion coefficient \(\gamma\), with ${\(0.5, 1, 1.5, 2, 2.5, 3, 3.5, 4, 4.5, 5\)}$. Risky equity portfolios include the market portfolio and six size and book-to-market equity sorted portfolios, BH, BM, BL, SH, SM and SL. The annualised certainty equivalent gain is the monthly certainty equivalent gain multiplied by twelve. The sample period is from February 1966 to December 2019 and the out-of-sample period starts in March 1986.

## [1] "Market"
## ##------ Mon Aug  8 10:50:45 2022 ------##
## [1] "B_Q1"
## ##------ Mon Aug  8 10:50:45 2022 ------##
## [1] "B_Q2"
## ##------ Mon Aug  8 10:50:45 2022 ------##
## [1] "B_Q3"
## ##------ Mon Aug  8 10:50:45 2022 ------##
## [1] "B_Q4"
## ##------ Mon Aug  8 10:50:45 2022 ------##
## [1] "B_Q5"
## ##------ Mon Aug  8 10:50:46 2022 ------##
## [1] "S_Q1"
## ##------ Mon Aug  8 10:50:46 2022 ------##
## [1] "S_Q2"
## ##------ Mon Aug  8 10:50:46 2022 ------##
## [1] "S_Q3"
## ##------ Mon Aug  8 10:50:46 2022 ------##
## [1] "S_Q4"
## ##------ Mon Aug  8 10:50:46 2022 ------##
## [1] "S_Q5"
## ##------ Mon Aug  8 10:50:46 2022 ------##

## Warning: Removed 4 rows containing missing values (geom_point).

## Warning: Removed 4 row(s) containing missing values (geom_path).

Table 5 - MSPE-adjusted Statistic

MSPE-adjusted Statistic

This table presents the MSEP-adjusted Statistics, evaluating the statistical significance of the out-of-sample R-squared statistics of each model in the corresponding portfolio.

See Rapach et al., (2010) and Clark and West (2007) for the detailed procedure.

table5.df <- data.frame()
for (port in names(TABLE5)) {
  pt <- TABLE5[[port]]
  pt$rowname <- rownames(pt)
  pt$portname <- port
  colnames(pt)[4] <- "star"
  table5.df <- rbind.data.frame(table5.df, pt)
}

table5.output <- gt(table5.df, rowname_col = "rowname", groupname_col = "portname") %>%
  fmt_percent(columns = vars(OOS_r.squared, mspe_pvalue), decimals = 2) %>%
  fmt_number(columns = vars(mspe_t), decimals = 4) %>%
  tab_header(title = "Table 5 - MSPE-adjusted Statistic",
             subtitle = paste(str_to_title(freq_name(freq = freq)), " data starts from ", first(data_decompose$month), " and ends in ", last(data_decompose$month), ".", sep = ""))

## Warning: `columns = vars(...)` has been deprecated in gt 0.3.0:
## * please use `columns = c(...)` instead

## Warning: `columns = vars(...)` has been deprecated in gt 0.3.0:
## * please use `columns = c(...)` instead

## Warning: `columns = vars(...)` has been deprecated in gt 0.3.0:
## * please use `columns = c(...)` instead

## Warning: `columns = vars(...)` has been deprecated in gt 0.3.0:
## * please use `columns = c(...)` instead

table5.output

Table 5 - MSPE-adjusted Statistic
Monthly data starts from Jan 1967 and ends in Dec 2019.
	OOS_r.squared	mspe_t	mspe_pvalue	star
Market
DP	−1.04%	0.7486	22.73%
PE	−0.23%	0.6344	26.31%
EY	−0.17%	0.7180	23.66%
DY	−0.86%	0.9945	16.03%
Payout	−0.79%	−1.4743	92.94%
SOP	0.47%	1.2007	11.53%
B_Q1
DP	−0.86%	0.4784	31.63%
PE	−0.45%	−0.1179	54.69%
EY	−0.40%	−0.0658	52.62%
DY	−0.65%	0.7265	23.40%
Payout	−0.41%	0.9244	17.79%
SOP	0.20%	0.8330	20.27%
B_Q2
DP	−1.73%	0.9680	16.68%
PE	0.08%	0.7098	23.91%
EY	0.17%	0.7927	21.42%
DY	−1.41%	1.0857	13.91%
Payout	−0.43%	−0.9140	81.94%
SOP	1.16%	2.1657	1.55%	**
B_Q3
DP	−2.59%	0.3446	36.53%
PE	−0.15%	0.5696	28.46%
EY	−0.08%	0.5900	27.78%
DY	−2.05%	0.4692	31.96%
Payout	−0.35%	−1.6596	95.11%
SOP	−0.04%	0.6390	26.16%
B_Q4
DP	−1.41%	0.2388	40.57%
PE	−0.24%	0.5521	29.06%
EY	−0.16%	0.6050	27.28%
DY	−1.24%	0.3389	36.74%
Payout	−0.54%	−0.5360	70.39%
SOP	0.35%	1.0601	14.49%
B_Q5
DP	−0.29%	0.4226	33.64%
PE	0.52%	1.3298	9.22%	*
EY	0.62%	1.4010	8.10%	*
DY	−0.21%	0.6217	26.72%
Payout	−0.36%	−0.7271	76.62%
SOP	0.28%	1.1005	13.59%
S_Q1
DP	−1.35%	−1.0046	84.22%
PE	−0.14%	−1.4006	91.89%
EY	−0.10%	−0.9129	81.91%
DY	−1.69%	−0.2823	61.11%
Payout	−0.20%	−0.6779	75.09%
SOP	−1.86%	−0.1594	56.33%
S_Q2
DP	−1.19%	1.2148	11.26%
PE	−1.41%	−0.4466	67.23%
EY	−1.62%	−0.1044	54.15%
DY	−1.58%	1.4142	7.90%	*
Payout	−1.16%	−0.5508	70.90%
SOP	−0.57%	0.4283	33.43%
S_Q3
DP	−1.06%	−0.2551	60.06%
PE	−0.36%	0.8173	20.71%
EY	−0.45%	1.0611	14.46%
DY	−1.50%	0.1049	45.83%
Payout	−0.73%	−0.7989	78.76%
SOP	−0.44%	−0.0838	53.34%
S_Q4
DP	−1.55%	0.6738	25.04%
PE	−0.28%	0.7719	22.03%
EY	−0.33%	1.0887	13.85%
DY	−2.22%	1.0050	15.77%
Payout	−0.71%	−1.5943	94.42%
SOP	−0.21%	0.2801	38.98%
S_Q5
DP	−2.18%	0.4519	32.58%
PE	0.69%	1.8153	3.51%	**
EY	0.80%	2.0387	2.11%	**
DY	−2.74%	0.8411	20.04%
Payout	−1.35%	−1.1671	87.81%
SOP	−0.31%	0.4683	31.99%